Extracellular vesicles (EVs) are believed to be an important means of cell-to-cell communication in the central nervous system. Various types of cells in the human brain secrete EVs, which are each likely to have distinct functions. Studying different types of EVs and their roles in the healthy brain and in neurological disease requires a reliable means of capturing these particles from readily accessible body fluids, like blood. Our goal is to develop a new way to capture specific types of EVs based on the molecules present on their surfaces. Our hypothesis is that we can use the presence of two or more specific molecules on the surface of an EV to select EVs secreted by a particular cell type. MSD has previously developed efficient and highly sensitive methods for screening EVs to determine combinations of proteins present on their surfaces. We will use these methods along with samples provided by our collaborators at the National Institute on Aging to identify specific surface-marker signatures for each of the four most common types of cells in the central nervous system. We will develop a new approach to capturing EVs with each of these surface marker signatures. This approach, which will only capture the EVs having all of the targeted surface markers, should greatly improve the capture specificity, thus addressing one of the main shortcomings of the existing CNS EV isolation methods. Greater specificity will enable more targeted studies of the EVs from each CNS cell type than those that are presently performed. We will use this approach to determine which human biofluids can be a reliable source of EVs from the central nervous system and to estimate the level of each type of EV in peripheral body fluids. We will also measure the proteins contained within the EVs, something that requires exceptionally sensitive assays. We recently combined ultrasensitive immunoassays with EV- specific capture methods to enable accurate measurement of specific EV cargo proteins. We will assay the EV cargo for proteins specific to each of the four CNS cell types to help confirm the cellular origin of the isolated EVs. We will also measure known EV-associated biomarkers of Alzheimer?s disease in patient samples to determine in which EV types they are enriched and to see whether measuring the biomarker within a particular type of EV might increase its predictive power. Lastly we plan to develop a fully automated version of the EV isolation and cargo protein assays. This will enable rapid and reliable preparation of EV samples from hundreds of samples at a time, allowing studies that presently would be either impossible or too time- consuming to be cost-effective.